AI Insights

• There is a growing concern about the potential risks associated with large language models (LLMs), such as bias, misinformation, and harm to users. (ML: 0.99)👍👎
• There is a need to address the potential risks associated with LLMs, such as bias, misinformation, and harm to users. (ML: 0.98)👍👎
• Human-AI Coevolution: The process by which humans and AI systems influence each other's development and behavior. (ML: 0.98)👍👎
• Lack of transparency in LLMs' decision-making processes. (ML: 0.97)👍👎
• Researchers are exploring various applications of conversational AI, including mental health chatbots and human-centered AI for mental health. (ML: 0.94)👍👎
• The field of conversational AI has seen significant advancements in recent years, with a focus on social and ethical considerations. (ML: 0.94)👍👎
• The field of conversational AI is rapidly evolving, with a focus on social and ethical considerations. (ML: 0.94)👍👎
• Researchers have explored various applications of conversational AI, including mental health chatbots, online CBT treatment, and human-centered AI for mental health. (ML: 0.93)👍👎
• Conversational AI: A type of artificial intelligence that enables humans to interact with machines using natural language. (ML: 0.93)👍👎
• Large Language Models (LLMs): Deep learning models that can process and generate human-like text. (ML: 0.90)👍👎

Abstract
The integration of Conversational Agents (CAs) into daily life offers opportunities to tackle global challenges, leading to the emergence of Conversational AI for Social Good (CAI4SG). This paper examines the advancements of CAI4SG using a role-based framework that categorizes systems according to their AI autonomy and emotional engagement. This framework emphasizes the importance of considering the role of CAs in social good contexts, such as serving as empathetic supporters in mental health or functioning as assistants for accessibility. Additionally, exploring the deployment of CAs in various roles raises unique challenges, including algorithmic bias, data privacy, and potential socio-technical harms. These issues can differ based on the CA's role and level of engagement. This paper provides an overview of the current landscape, offering a role-based understanding that can guide future research and design aimed at the equitable, ethical, and effective development of CAI4SG.

Why we are recommending this paper?
Due to your Interest in Tech for Social Good

This paper directly addresses the user's interest in AI for Social Good, exploring the application of Conversational AI to tackle global challenges. Given the growing interest in leveraging technology for positive social impact, this offers a valuable overview of the field.

AI Insights

• This illustrates the importance of modeling how non-participating agents react to coalition deviations. (ML: 0.93)👍👎
• The paper also discusses related work on cooperative game theory, strategic behavior in voting systems, and computational social choice. (ML: 0.93)👍👎
• The paper discusses a model of strategic behavior in which agents can revise their contracts and votes simultaneously, while non-participating agents update their votes optimally in response to the new contract scheme. (ML: 0.89)👍👎
• It demonstrates that IR-SNE may fail to exist under sticky strategic behavior, even for simple scenarios with two agents and two alternatives. (ML: 0.86)👍👎
• A variant of this model, called 'sticky' strategic behavior, is also considered, where non-participating agents do not respond to changes in their utilities induced by revised contracts. (ML: 0.86)👍👎
• IR-SNE: Individual Rationality and Strategic Nash Equilibrium Sticky Strategic Behavior: A variant of the main model where non-participating agents do not respond to changes in their utilities induced by revised contracts. (ML: 0.86)👍👎
• The main model is analyzed using a specific example with two agents, two alternatives, and the consensus rule, demonstrating that IR-SNE can exist under non-sticky strategic behavior but not under sticky strategic behavior. (ML: 0.85)👍👎
• Non-Sticky Strategic Behavior: The main model, where all agents in a coalition can simultaneously revise their contracts and votes, while non-participating agents update their votes optimally in response to the new contract scheme. (ML: 0.84)👍👎
• The paper highlights the importance of considering how non-participating agents react to coalition deviations when analyzing strategic behavior. (ML: 0.84)👍👎
• This is referred to as 'main' or 'non-sticky' strategic behavior. (ML: 0.81)👍👎
• The paper shows that IR-SNE may fail to exist under sticky strategic behavior, even for two agents, two alternatives, and the consensus rule. (ML: 0.81)👍👎

Abstract
Many multiagent systems rely on collective decision-making among self-interested agents, which raises deep questions about coalition formation and stability. We study social choice with endogenous, outcome-contingent transfers, where agents voluntarily form contracts that redistribute utility depending on the collective decision, allowing fully strategic, incentive-aligned coalition formation. We show that under consensus rules, individually rational strong Nash equilibria (IR-SNE) always exist, implementing welfare-maximizing outcomes with feasible transfers, and provide a simple, efficient algorithm to construct them. For more general anonymous, monotonic, and resolute rules, we identify necessary conditions for profitable deviations, sharply limiting destabilizing coalitions. By bridging cooperative and noncooperative perspectives, our approach shows that transferable utility can achieve core-like stability, restoring efficiency and budget balance even where classical impossibility results apply. Overall, this framework offers a practical and robust way to coordinate large-scale strategic multiagent systems.

Why we are recommending this paper?
Due to your Interest in Econometrics for Social Good

This research focuses on social choice and welfare optimization, aligning with the user’s interest in measuring and addressing inequality. The concept of endogenous transfers provides a framework for understanding how to improve outcomes for individuals within complex systems.

AI Insights

• Higher RCP values indicate greater climate change impacts, while lower values suggest less severe effects. (ML: 0.93)👍👎
• Some countries have higher RCP values, such as Mexico (0.14955) and Nigeria (0.05318), suggesting they are more vulnerable to climate change due to their high greenhouse gas emissions. (ML: 0.92)👍👎
• The data also includes some small island nations, such as the Marshall Islands (0.00003) and Maldives (0.00029), which may be more susceptible to climate-related issues due to their geographical location. (ML: 0.92)👍👎
• Country codes: Two-letter country codes, such as 'US' for the United States or 'CN' for China. (ML: 0.90)👍👎
• The data provides a snapshot of countries' vulnerability to climate change based on their greenhouse gas emissions. (ML: 0.90)👍👎
• Other countries have lower RCP values, like Liechtenstein (0.00033) and Monaco (0.00042), indicating they are less affected by climate change. (ML: 0.89)👍👎
• Values: RCP values represent the concentration of carbon dioxide in the atmosphere and are used to estimate climate change impacts. (ML: 0.88)👍👎
• The RCP values range from 0.03 to 8.55, indicating varying levels of carbon dioxide emissions and climate change impacts across different regions. (ML: 0.85)👍👎
• RCP: Representative Concentration Pathway - a measure of greenhouse gas emissions used to estimate climate change impacts. (ML: 0.84)👍👎
• The data appears to be a list of countries with their corresponding RCP (Representative Concentration Pathway) values, which are used to estimate the concentration of greenhouse gases in the atmosphere. (ML: 0.80)👍👎

Abstract
We estimate the national social cost of carbon using a recent meta-analysis of the total impact of climate change and a standard integrated assessment model. The average social cost of carbon closely follows per capita income, the national social cost of carbon the size of the population. The national social cost of carbon measures self-harm. Net liability is defined as the harm done by a country's emissions on other countries minus the harm done to a country by other countries' emissions. Net liability is positive in middle-income, carbon-intensive countries. Poor and rich countries would be compensated because their current emissions are relatively low, poor countries additionally because they are vulnerable.

Why we are recommending this paper?
Due to your Interest in Econometrics for Social Good

This paper investigates the social cost of carbon, a critical factor in understanding and mitigating climate change – a key concern for the user’s interest in a healthy society. The use of economic modeling to quantify this cost is directly relevant to their interest in measurable ways to end poverty.

AI Insights

• This could lead to difficulties in applying the result to more general cases. (ML: 0.98)👍👎
• Let G be a graph with vertex set V(G) and edge set E(G). (ML: 0.93)👍👎
• The problem statement involves deriving a bound on the number of edges in a graph G, given certain properties of the graph. (ML: 0.90)👍👎
• A d-set is defined as a subset S ⊆ V(G) such that |S| = d. (ML: 0.89)👍👎
• The final answer is a bound on the number of edges in G, expressed in terms of |V(G)| and other parameters. (ML: 0.88)👍👎
• The solution relies heavily on the properties of the graph G, which may not be explicitly stated. (ML: 0.88)👍👎
• This leads to a bound on the number of edges in G. (ML: 0.85)👍👎
• The parameter t_d is defined as the number of d-sets in G, i.e., t_d = |F_d|. (ML: 0.84)👍👎
• The goal is to find an upper limit for |E(G)| based on the size of the vertex set V(G). (ML: 0.81)👍👎
• The solution involves using Shearer's lemma and the concept of d-sets to derive an inequality involving the entropy of the random variable Xn. (ML: 0.81)👍👎
• The final answer is derived by manipulating the inequality obtained from Shearer's lemma, which ultimately yields an expression for |E(G)| in terms of |V(G)| and other parameters. (ML: 0.79)👍👎
• The solution involves using Shearer's lemma to derive an inequality involving the entropy of Xn, which ultimately leads to the desired bound on |E(G)|. (ML: 0.74)👍👎
• The parameters m_d and ℓ_d are defined as follows: m_d = 2^d and ℓ_d = n - d + 1. (ML: 0.72)👍👎

Abstract
It is well known that there is a strong connection between entropy inequalities and submodularity, since the entropy of a collection of random variables is a submodular function. Unifying frameworks for information inequalities arising from submodularity were developed by Madiman and Tetali (2010) and Sason (2022). Madiman and Tetali (2010) established strong and weak fractional inequalities that subsume classical results such as Han's inequality and Shearer's lemma. Sason (2022) introduced a convex-functional framework for generalizing Han's inequality, and derived unified inequalities for submodular and supermodular functions. In this work, we build on these frameworks and make three contributions. First, we establish convex-functional generalizations of the strong and weak Madiman and Tetali inequalities for submodular functions. Second, using a special case of the strong Madiman-Tetali inequality, we derive a new Loomis-Whitney-type projection inequality for finite point sets in $\mathbb{R}^d$, which improves upon the classical Loomis-Whitney bound by incorporating slice-level structural information. Finally, we study an extremal graph theory problem that recovers and extends the previously known results of Sason (2022) and Boucheron et al., employing Shearer's lemma in contrast to the use of Han's inequality in those works.

Why we are recommending this paper?
Due to your Interest in Inequality

This paper explores the connection between information theory and submodularity, a foundational area relevant to understanding data analysis and optimization. The focus on combinatorial problems is likely to be useful for developing more efficient solutions to social good challenges.

AI Insights

• Cognitive effort: The mental resources required by participants to understand and interpret KRIYA's outputs. (ML: 0.99)👍👎
• Co-interpretation: The process of interpreting data with the help of KRIYA's conversational interactions. (ML: 0.99)👍👎
• The study highlights the importance of non-judgmental language, transparency around uncertainty, and credibility in building trust with users. (ML: 0.99)👍👎
• Interpretive depth: The level of detail and complexity in KRIYA's explanations, which can be burdensome if too high. (ML: 0.98)👍👎
• Credibility: The extent to which participants trusted KRIYA's interpretations and explanations. (ML: 0.98)👍👎
• Credibility was evaluated by checking whether KRIYA's explanations aligned with their own lived experience. (ML: 0.98)👍👎
• The system's ability to communicate its reasoning and explain why a particular conclusion was being suggested increased trust. (ML: 0.98)👍👎
• The study found that participants appreciated the non-judgmental language used in KRIYA, which lowered the barrier to reflection. (ML: 0.97)👍👎
• Participants were clear about where they wanted the system to stop inferring, and trust could diminish when errors distorted core signals or when explanations extended beyond available evidence. (ML: 0.97)👍👎
• Participants valued transparency around uncertainty, as it reframed trust as something grounded in communicative openness rather than factual perfection. (ML: 0.96)👍👎

Abstract
Most personal wellbeing apps present summative dashboards of health and physical activity metrics, yet many users struggle to translate this information into meaningful understanding. These apps commonly support engagement through goals, reminders, and structured targets, which can reinforce comparison, judgment, and performance anxiety. To explore a complementary approach that prioritizes self-reflection, we design KRIYA, an AI wellbeing companion that supports co-interpretive engagement with personal wellbeing data. KRIYA aims to collaborate with users to explore questions, explanations, and future scenarios through features such as Comfort Zone, Detective Mode, and What-If Planning. We conducted semi-structured interviews with 18 college students interacting with a KRIYA prototype using hypothetical data. Our findings show that through KRIYA interaction, users framed engaging with wellbeing data as interpretation rather than performance, experienced reflection as supportive or pressuring depending on emotional framing, and developed trust through transparency. We discuss design implications for AI companions that support curiosity, self-compassion, and reflective sensemaking of personal health data.

Why we are recommending this paper?
Due to your Interest in AI for Social Good

This paper presents an AI companion designed for wellbeing, addressing the user's interest in healthy society and AI for social good. The focus on self-reflection and understanding personal metrics aligns with the desire to improve individual wellbeing.